What kind of simulated environment is complex enough to develop a general AI?
I think this is one of the best AGI related questions I've see in this forum.

I will skip all thematic about "what is an AGI", "simulation game", ... These topics have been discussed during decades and nowadays they are, in my opinion, a dead end.

Thus, I can only answer with my personal experience:

It is a basic theorem in computing that any number of dimensions, including temporal one, in a finite size space, can be reduced to 1D. 

However, in practical examples, the 1D representation becomes hard to analyze and visualize. It is more practical work with graphs, that can be seen as an intermediate between 1D and 2D. Graphs allows representation of all necessary facts and relations.

By example, if we try to develop an AGI able to work in the area of mathematics, any expression (that humans we write in a 2D representation with rationals, subscripts, integrals, ...) can be represented as 1D (as an expression written in a program source) but this 1D must be parsed to reach the graph that can be analyzed or executed. Thus, the graph that results after the parsing of the expression is the most practical representation.

Another example, if we want an agent that travels across a 3D world, this world can be seen as an empty space with objects that have some properties. Again, after the initial stage of scene analysis and object recognition (the equivalent to the parser in previous example), we reach a graph.

Thus, to really work in the area of AGI, I suggest skip the problems of scene analysis, object recognition, speech recognition (Narrow AI), and work directly over the representative graphs.